SENTIMENT ANALYSIS OF USER REVIEWS OF THE SENTUH TANAHKU APPLICATION
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Abstrak
Aplikasi Sentuh Tanahku menjadi salah satu instrumen layanan informasi pertanahan secara digital yang diinisiasi oleh Kementerian Agraria dan Tata Ruang/Kepala Badan Pertanahan Nasional sejak tahun 2017. Selama masa penggunaan Aplikasi Sentuh Tanahku dari tahun 2017 hingga sekarang tahun 2026, aplikasi ini sudah diunduh lebih dari 1 juta kali, dengan rating sebanyak 47 ribu ulasan. Penelitian ini membaca 19.927 ulasan Google Playstore periode 21 September 2017 sampai 13 Mei 2026 untuk mengetahui pola sentimen, kinerja model klasifikasi, dan aspek layanan yang paling sering memengaruhi penilaian pengguna. Analisis dilakukan melalui pra-pemrosesan teks, pelabelan berbasis skor, leksikon sentimen, TF-IDF, Multinomial Naive Bayes, Linear Support Vector Machine, serta pemetaan aspek layanan. Data menunjukkan pola penilaian yang terbelah. Skor 5 mencapai 57,93%, sedangkan skor 1 mencapai 23,97%, dengan rata-rata 3,70. Analisis leksikon menemukan 9.605 ulasan positif, 6.537 negatif, dan 3.785 netral. Linear SVM menghasilkan macro F1-score terbaik sebesar 62,29%. Keluhan paling menonjol muncul pada autentikasi akun, performa aplikasi, serta keterhubungan sertipikat dan data bidang. Temuan ini menegaskan bahwa kualitas aplikasi pertanahan bergantung pada stabilitas teknis, alur verifikasi, dan kesiapan data layanan.
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Sitasi
Kusmiarto, K., & Wahyuni , W. (2026). SENTIMENT ANALYSIS OF USER REVIEWS OF THE SENTUH TANAHKU APPLICATION. KHARISMA Tech, 21(2), 200-214. https://doi.org/10.55645/kharismatech.v21i2.735
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Referensi
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[8] C. Sentosa, Sudirman, and Afifah, “Analisis kepuasan pengguna terhadap website Kharisma Classroom menggunakan metode PIECES,” KHARISMA Tech, vol. 19, no. 2, pp. 98-112, 2024, doi: 10.55645/kharismatech.v19i2.409.
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[12] A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117-126, 2016, doi: 10.1016/j.eswa.2016.03.028.
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[16] U. Khaira, R. Johanda, P. E. P. Utomo, and T. Suratno, “Sentiment Analysis of Cyberbullying on Twitter Using SentiStrength,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 3, no. 1, pp. 21-27, 2020.
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[19] M. A. Shareef, Y. K. Dwivedi, N. P. Rana, and R. Raman, “SQ mGov: A comprehensive service-quality paradigm for mobile government,” Information Systems Management, vol. 31, no. 2, pp. 126-142, 2014, doi: 10.1080/10580530.2014.890432.
[20] A. J. Desmal, “Exploring the information quality of mobile government services,” PeerJ Computer Science, vol. 8, e1028, 2022, doi: 10.7717/peerj-cs.1028.
[21] Z. Mao, Q. Zou, T. Bu, Y. Dong, and R. Yan, “Understanding the role of service quality of government APPs in continuance intention: An expectation-confirmation perspective,” SAGE Open, vol. 13, no. 4, 2023, doi: 10.1177/21582440231201218.
[22] N. Xu and W. Zhang, “User satisfaction with Chinese government apps: Topic mining and sentiment analysis of user reviews,” Lex Localis - Journal of Local Self-Government, pp. 95-124, 2025, doi: 10.52152/23.3.95-124(2025).
[23] T. Liu, C. Wang, K. Huang, P. Liang, B. Zhang, M. Daneva, and M. van Sinderen, “ROSEMATCHER: Identifying the impact of user reviews on app updates,” Information and Software Technology, vol. 161, 107261, 2023, doi: 10.1016/j.infsof.2023.107261.
[24] D. Pagano and W. Maalej, “User feedback in the app store: An empirical study,” in Proc. 21st IEEE International Requirements Engineering Conference, 2013, pp. 125-134, doi: 10.1109/RE.2013.6636712.
[25] E. Guzman and W. Maalej, “How do users like this feature? A fine grained sentiment analysis of app reviews,” in Proc. 22nd IEEE International Requirements Engineering Conference, 2014, pp. 153-162, doi: 10.1109/RE.2014.6912257.
[26] I. Williamson, S. Enemark, J. Wallace, and A. Rajabifard, Land Administration for Sustainable Development. Redlands, CA: ESRI Press Academic, 2010.
[27] S. Enemark, R. McLaren, and C. Lemmen, Fit-for-Purpose Land Administration: Guiding Principles for Country Implementation. Nairobi: UN-Habitat/GLTN, 2016.
[28] Z. Zeng, S. Li, J. W. Lian, J. Li, T. Chen, and Y. Li, “Switching behavior in the adoption of a land information system in China: A perspective of the push-pull-mooring framework,” Land Use Policy, vol. 109, 105629, 2021, doi: 10.1016/j.landusepol.2021.105629.
[2] Google Play, “Sentuh Tanahku,” 2026. [Online]. Available: https://play.google.com/store/apps/details?id=id.go.bpn.sentuh. Accessed: May 13, 2026.
[3] W. Winarto, I. Alwiah Musdar, and H. Hasniati, “Sentiment analysis of 2024 presidential candidate using the Support Vector Machine algorithm on Twitter,” KHARISMA Tech, vol. 19, no. 1, pp. 86-98, 2024.
[4] K. E. Hadiputra, B. Zaman, and S. Bahri, “Analysis of service quality of Beli.in application using the PIECES framework method,” KHARISMA Tech, vol. 19, no. 2, pp. 58-71, 2024, doi: 10.55645/kharismatech.v19i2.474.
[5] F. A. Tejokusuma, H. Angriani, and Afifah, “Analisis tingkat kepuasan pengguna terhadap aplikasi TIERRA menggunakan metode PIECES Framework,” KHARISMA Tech, vol. 17, no. 2, pp. 157-171, 2022, doi: 10.55645/kharismatech.v17i2.312.
[6] C. V. Wu, Hasniati, and I. Alwiah Musdar, “Implementation of User Centered Design approach in User Interface design and User Experience website worker’s,” KHARISMA Tech, vol. 17, no. 2, pp. 71-84, 2022, doi: 10.55645/kharismatech.v17i2.246.
[7] C. Crystanto, A. Munir S., and H. Surasa, “Analisis kepuasan pengguna aplikasi MyTelkomsel menggunakan PIECES Framework,” KHARISMA Tech, vol. 19, no. 1, pp. 26-38, 2024, doi: 10.55645/kharismatech.v19i1.453.
[8] C. Sentosa, Sudirman, and Afifah, “Analisis kepuasan pengguna terhadap website Kharisma Classroom menggunakan metode PIECES,” KHARISMA Tech, vol. 19, no. 2, pp. 98-112, 2024, doi: 10.55645/kharismatech.v19i2.409.
[9] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proc. EMNLP, 2002, pp. 79-86.
[10] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA: Morgan & Claypool Publishers, 2012.
[11] M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment strength detection for the social web,” J. Am. Soc. Inf. Sci. Technol., vol. 63, no. 1, pp. 163-173, 2012, doi: 10.1002/asi.21662.
[12] A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117-126, 2016, doi: 10.1016/j.eswa.2016.03.028.
[13] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171-4186.
[14] B. Wilie dkk., “IndoNLU: Benchmark and resources for evaluating Indonesian natural language understanding,” in Proc. AACL-IJCNLP, 2020, pp. 843-857.
[15] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” in Proc. COLING, 2020, pp. 757-770.
[16] U. Khaira, R. Johanda, P. E. P. Utomo, and T. Suratno, “Sentiment Analysis of Cyberbullying on Twitter Using SentiStrength,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 3, no. 1, pp. 21-27, 2020.
[17] H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. R. Arifin, and K. Kusrini, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 348-354, 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
[18] F. Pedregosa dkk., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
[19] M. A. Shareef, Y. K. Dwivedi, N. P. Rana, and R. Raman, “SQ mGov: A comprehensive service-quality paradigm for mobile government,” Information Systems Management, vol. 31, no. 2, pp. 126-142, 2014, doi: 10.1080/10580530.2014.890432.
[20] A. J. Desmal, “Exploring the information quality of mobile government services,” PeerJ Computer Science, vol. 8, e1028, 2022, doi: 10.7717/peerj-cs.1028.
[21] Z. Mao, Q. Zou, T. Bu, Y. Dong, and R. Yan, “Understanding the role of service quality of government APPs in continuance intention: An expectation-confirmation perspective,” SAGE Open, vol. 13, no. 4, 2023, doi: 10.1177/21582440231201218.
[22] N. Xu and W. Zhang, “User satisfaction with Chinese government apps: Topic mining and sentiment analysis of user reviews,” Lex Localis - Journal of Local Self-Government, pp. 95-124, 2025, doi: 10.52152/23.3.95-124(2025).
[23] T. Liu, C. Wang, K. Huang, P. Liang, B. Zhang, M. Daneva, and M. van Sinderen, “ROSEMATCHER: Identifying the impact of user reviews on app updates,” Information and Software Technology, vol. 161, 107261, 2023, doi: 10.1016/j.infsof.2023.107261.
[24] D. Pagano and W. Maalej, “User feedback in the app store: An empirical study,” in Proc. 21st IEEE International Requirements Engineering Conference, 2013, pp. 125-134, doi: 10.1109/RE.2013.6636712.
[25] E. Guzman and W. Maalej, “How do users like this feature? A fine grained sentiment analysis of app reviews,” in Proc. 22nd IEEE International Requirements Engineering Conference, 2014, pp. 153-162, doi: 10.1109/RE.2014.6912257.
[26] I. Williamson, S. Enemark, J. Wallace, and A. Rajabifard, Land Administration for Sustainable Development. Redlands, CA: ESRI Press Academic, 2010.
[27] S. Enemark, R. McLaren, and C. Lemmen, Fit-for-Purpose Land Administration: Guiding Principles for Country Implementation. Nairobi: UN-Habitat/GLTN, 2016.
[28] Z. Zeng, S. Li, J. W. Lian, J. Li, T. Chen, and Y. Li, “Switching behavior in the adoption of a land information system in China: A perspective of the push-pull-mooring framework,” Land Use Policy, vol. 109, 105629, 2021, doi: 10.1016/j.landusepol.2021.105629.